tvreg: Variational Imaging Methods. The tvreg package performs total variation (TV) regularized image denoising, deconvolution, and inpainting. Three different noise models are supported: Gaussian (L2), Laplace (L1), and Poisson. The implementation solves the general TV restoration problem: min_u TV(u) + int lambda F(K*u,f) dx .to perform denoising, deconvolution, and inpainting as special cases. It is efficiently solved using the recent split Bregman method. Also included is an efficient implementation of Chan-Vese two-phase segmentation. All functions support grayscale, color, and arbitrary multichannel images.

References in zbMATH (referenced in 27 articles )

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  1. Archibald, Richard; Tran, Hoang: A dictionary learning algorithm for compression and reconstruction of streaming data in preset order (2022)
  2. Barbu, Tudor: Nonlinear PDE-based models for photon-limited image restoration (2021)
  3. Mukhoty, Bhaskar; Dutta, Subhajit; Kar, Purushottam: Robust non-parametric regression via incoherent subspace projections (2021)
  4. Pchelintsev, I. A.; Nasonov, A. V.; Krylov, A. S.: Regularization methods in the analysis of a series of scintillation fluorescence microscopy images (2021)
  5. Xu, Maoyuan; Xie, Xiaoping: An efficient feature-preserving image denoising algorithm based on a spatial-fractional anisotropic diffusion equation (2021)
  6. Holler, Martin; Weinmann, Andreas: Non-smooth variational regularization for processing manifold-valued data (2020)
  7. Kumar, Sumit; Jha, Rajib Kumar: An FPGA-based design for a real-time image denoising using approximated fractional integrator (2020)
  8. Mead, J.: ( \chi^2) test for total variation regularization parameter selection (2020)
  9. Ben Said, Ahmed; Hadjidj, Rachid; Foufou, Sebti: Total variation for image denoising based on a novel smart edge detector: an application to medical images (2019)
  10. Legarda-Saenz, Ricardo; Téllez Quiñones, Alejandro; Brito-Loeza, Carlos; Espinosa-Romero, Arturo: Variational phase recovering without phase unwrapping in phase-shifting interferometry (2019)
  11. Wang, Wei; Xia, Xiang-Gen; Zhang, Shengli; He, Chuanjiang; Chen, Ling: Vector total fractional-order variation and its applications for color image denoising and decomposition (2019)
  12. You, Juntao; Jiao, Yuling; Lu, Xiliang; Zeng, Tieyong: A nonconvex model with minimax concave penalty for image restoration (2019)
  13. Campagna, Rosanna; Crisci, Serena; Cuomo, Salvatore; Marcellino, Livia; Toraldo, Gerardo: Modification of TV-ROF denoising model based on split Bregman iterations (2017)
  14. Borkowski, Dariusz: Forward and backward filtering based on backward stochastic differential equations (2016)
  15. Lu, Wenqi; Duan, Jinming; Qiu, Zhaowen; Pan, Zhenkuan; Liu, Ryan Wen; Bai, Li: Implementation of high-order variational models made easy for image processing (2016)
  16. Maiseli, Baraka Jacob; Gao, Huijun: Robust edge detector based on anisotropic diffusion-driven process (2016)
  17. Orović, Irena; Lekić, Nedjeljko; Stanković, Srdjan: An analog-digital hardware for L-estimate space-varying image filtering (2016)
  18. Coll, Bartomeu; Duran, Joan; Sbert, Catalina: Half-linear regularization for nonconvex image restoration models (2015)
  19. Batard, Thomas; Bertalmío, Marcelo: On covariant derivatives and their applications to image regularization (2014)
  20. Burger, M.; Müller, J.; Papoutsellis, E.; Schönlieb, C. B.: Total variation regularization in measurement and image space for PET reconstruction (2014)

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